Electrochemistry on the Edge: Advancing High Oxidation Power Materials for Applications at Strongly Oxidizing Potentials
Bibliographic record
Abstract
High oxidation power (HOP) materials such as lead(IV) oxide (PbO2), antimony-doped tin(IV) oxide (ATO), Magnéli phase titanium oxides (MPTOs), and boron-doped diamond (BDD) are important for advancing electrochemical technologies for clean water and energy applications. Specifically, these include electrocatalytic materials for electrolyzers for water treatment applications and electrosynthesis applications, supports for electrocatalysts for fuel cells and electrolyzers for hydrogen production and electrosyntheses, and electrically-conductive filler in batteries. However, issues of toxicity (PbO2), stability (ATO, MPTOs), and cost (BDD) must be addressed if HOPs are to be more-broadly used as alternatives to carbon-based materials. The Wilkinson group at the University of British Columbia has recently prepared transition metal doped Ti4O7, a MPTO, which exhibits improved characteristics as an electrode material [1,2]. Specifically, thermogravimetric analysis and electrochemical accelerated life testing (Figs. a & b, respectively), illustrate its superior resistance to oxidation while use of the 4-point probes method reveals greater electrical conductivity relative to pristine Ti4O7. The preparation of these materials and their characterization as well as their eventual incorporation into electrochemical devices for clean water and energy applications will be discussed. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".